10B.6 Evaluating the Impact of Grell-Freitas Convective Parameterization into Hurricanes Harvey, Irma, and Maria Simulations Using FV3GFS

Wednesday, 18 April 2018: 2:45 PM
Masters ABCD (Sawgrass Marriott)
Keren Rosado, NCAS/ESRL/GSD, Boulder, CO; and G. A. Grell, L. R. Bernardet, E. A. Kalina, and M. Fiorino

The National Oceanic and Atmospheric Administration (NOAA) has recently announced the selection of the Finite-Volume on a Cubed-Sphere (FV3) atmosphere dynamical core for its Next Generation Global Prediction System and is now engaged in defining the physics suites to be used in upcoming operational implementations of the FV3-based Global Forecast System (FV3GFS). In this investigation, we tested and evaluated the Grell-Freitas (GF) convective parameterization for the 2017 Atlantic hurricanes: Harvey, Irma, and Maria. Simulations for each hurricane were initiated four times a day, lasting for 120 hours for their entire life cycle. The impact of the GF physics and its interaction with other members of the FV3GFS physics suites will be addressed relative to observations. Preliminary results show that when a tropical cyclone is simulated using FV3GFS with GF convective parameterization, convective precipitation, as well as the total precipitation values are reasonable alongside observed total precipitation satellite derived from NOAA Climate Prediction Center Morphing Technic (CMORPH). These results were also compared alongside with simulations using FV3GFS with the Simplified Arakawa-Schubert (SAS) convective parameterization. Results of the comparison of these two convective parameterizations show that the tropical cyclone simulated using GF convective parameterization has more convective and total precipitation than the simulation using SAS convective parameterization. The analysis of tropical cyclone forecasts in FV3GFS will provide insight and understanding of the mesoscale and synoptic systems that directly impact track and intensity forecasts, therefore advancing the knowledge of mechanisms associated with forecast model errors.
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